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Few Shot Image Classification On Omniglot 5 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
Prototypical Networks98.9%Prototypical Networks for Few-shot Learning
GCR99.32Few-Shot Learning with Global Class Representations
VAMPIRE98.52%Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
ConvNet with Memory Module98.6%Learning to Remember Rare Events
Relation Net99.1%Learning to Compare: Relation Network for Few-Shot Learning
Matching Nets98.5%Matching Networks for One Shot Learning
DCN6-E99.63Decoder Choice Network for Meta-Learning
Hyperbolic ProtoNet98.15%Hyperbolic Image Embeddings
DCN499.5%Decoder Choice Network for Meta-Learning
Reptile + Transduction97.12%On First-Order Meta-Learning Algorithms
MAML++99.3%HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
MR-MAML94.1%Meta-Learning without Memorization
adaCNN (DF)98.43%Rapid Adaptation with Conditionally Shifted Neurons-
TapNet99.49%TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
MC2+99.65%Meta-Curvature
Neural Statistician98.1%Towards a Neural Statistician
APL97.6%Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
MAML++99.33%How to train your MAML
iMAML, Hessian-Free99.14%Meta-Learning with Implicit Gradients
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